Key factors selection on adolescents with non-suicidal self-injury: A support vector machine based approach Article Swipe
Jiaxin Yang
,
Yinghao Chen
,
Gongyu Yao
,
Zheng Wang
,
Fu Xi
,
Yusheng Tian
,
Yamin Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fpubh.2022.1049069
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fpubh.2022.1049069
Comparing a family structure to a company, one can often think of parents as leaders and adolescents as employees. Stressful family environments and anxiety levels, depression levels, personality disorders, emotional regulation difficulties, and childhood trauma may all contribute to non-suicidal self-injury (NSSI) behaviors. We presented a support vector machine (SVM) based method for discovering the key factors among mazy candidates that affected NSSI in adolescents. Using SVM as the base learner, and the binary dragonfly algorithm was used to find the feature combination that minimized the objective function, which took into account both the prediction error and the number of selected variables. Unlike univariate model analysis, we used a multivariate model to explore the risk factors, which better revealed the interactions between factors. Our research showed that adolescent education level, anxiety and depression level, borderline and avoidant personality traits, as well as emotional abuse and physical neglect in childhood, were associated with mood disorders in adolescents. Furthermore, gender, adolescent education level, physical abuse in childhood, non-acceptance of emotional responses, as well as paranoid, borderline, and histrionic personality traits, were associated with an increased risk of NSSI. These findings can help us make better use of artificial intelligence technology to extract potential factors leading to NSSI in adolescents from massive data, and provide theoretical support for the prevention and intervention of NSSI in adolescents.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fpubh.2022.1049069
- https://www.frontiersin.org/articles/10.3389/fpubh.2022.1049069/pdf
- OA Status
- gold
- Cited By
- 18
- References
- 73
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308874751
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4308874751Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fpubh.2022.1049069Digital Object Identifier
- Title
-
Key factors selection on adolescents with non-suicidal self-injury: A support vector machine based approachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-11-10Full publication date if available
- Authors
-
Jiaxin Yang, Yinghao Chen, Gongyu Yao, Zheng Wang, Fu Xi, Yusheng Tian, Yamin LiList of authors in order
- Landing page
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https://doi.org/10.3389/fpubh.2022.1049069Publisher landing page
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https://www.frontiersin.org/articles/10.3389/fpubh.2022.1049069/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/fpubh.2022.1049069/pdfDirect OA link when available
- Concepts
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Clinical psychology, Psychology, Anxiety, Poison control, Personality, Mood, Neglect, Psychiatry, Medicine, Social psychology, Medical emergencyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
- Citations by year (recent)
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2025: 9, 2024: 4, 2023: 5Per-year citation counts (last 5 years)
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73Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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